Mining data from 1000 genomes to identify the causal
variant in regions under positive selection
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Citation
Grossman, Shari et al. "Mining data from 1000 genomes to
identify the causal variant in regions under positive selection."
Genome Biology 2010, 11(Suppl 1):I22 (11 October 2010).
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http://genomebiology.com/2010/11/S1/I22
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BioMed Central Ltd.
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Final published version
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Wed May 25 15:28:15 EDT 2016
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http://hdl.handle.net/1721.1/69960
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Grossman et al. Genome Biology 2010, 11(Suppl 1):I22
http://genomebiology.com/2010/11/S1/I22
INVITED SPEAKER PRESENTATION
Open Access
Mining data from 1000 genomes to identify the
causal variant in regions under positive selection
Shari Grossman1,2,3*†, Ilya Shlyakhter1,2†, Elinor K Karlsson1,2, Shervin Tabrizi1,2, Kristian Andersen1,2, John Rinn2,
Eric Lander2, Steve Schaffner2, Pardis C. Sabeti1,2, The 1000 Genomes Project
From Beyond the Genome: The true gene count, human evolution and disease genomics
Boston, MA, USA. 11-13 October 2010
The human genome contains hundreds of regions in
which the patterns of genetic variation indicate recent
positive natural selection, yet for most of these the
underlying gene and the advantageous mutation remain
unknown. We recently reported the development of a
method, Composite of Multiple Signals (CMS), that
combines tests for multiple signals of natural selection
and increases resolution by up to 100-fold.
Applying CMS to candidate selected regions from the
International Haplotype Map, we localized several hundred signals to ~50-100 kb, identifying individual gene
and polymorphism targets of selection. These regions
included genes involved in processes known to be targets of selection, such as infectious disease, skin pigment, metabolism, and hair and sweat. We further
identified many candidates that are similar to regulatory
elements. In several regions, we identified variants that
are significantly associated with the expression of nearby
genes in the selected population. Moreover nearly half
of the ~200 regions we examined localized to regions
with no genes. Thirty of the regions contain long noncoding RNAs that have been shown to often regulate
nearby genes, suggesting that variation within the RNAs
might have functional consequences.
With preliminary data now available from the 1000
Genomes Project, we are beginning to explore full
sequence data, which should contains most if not all of
the causal selected polymorphisms. We extended the
CMS method to the preliminary data set, validating our
previously identified candidates and identifying many
new intriguing coding and regulatory variants.
Author details
1
Center for Systems Biology and Department of Organismic and Evolutionary
Biology, Cambridge, MA 02138, USA. 2Broad Institute of Harvard and MIT,
Cambridge, MA 02139 USA. 3Harvard Medical School, Boston, MA 0211, USA.
Published: 11 October 2010
doi:10.1186/gb-2010-11-S1-I22
Cite this article as: Grossman et al.: Mining data from 1000 genomes to
identify the causal variant in regions under positive selection. Genome
Biology 2010 11(Suppl 1):I22.
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† Contributed equally
1
Center for Systems Biology and Department of Organismic and Evolutionary
Biology, Cambridge, MA 02138, USA
Full list of author information is available at the end of the article
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